Spatial Sampling for Alignment of Robot Demonstrated Trajectories in Upper Limb Rehabilitation Tasks

Giovanni Braglia, Dario Onfiani, Davide Tebaldi, André Lazzaretti, Luigi Biagiotti

Keywords: Human-Robot Interaction, Motion Planning, Space-Time Analysis

Abstract: In robotics, Learning by Demonstration (LbD) aims to transfer skills to robots by leveraging multiple demonstrations of the same task. These demonstrations are stored in a library and processed to extract a consistent skill representation, typically requiring temporal alignment using techniques like Dynamic Time Warping (DTW). In this article, we propose a novel Spatial Sampling (SS) algorithm tailored for robot trajectories, which enables time-agnostic alignment by providing an arc-length parametrization of the input trajectories. This method eliminates the need for temporal alignment and enhances skill representation. We demonstrate the effectiveness of SS in an upper-limb rehabilitation case study, introducing a new human-robot interaction architecture.

Submission Number: 14